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Exploring Human Brain Functional Net...

Li, Shuoran,

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Exploring Human Brain Functional Networks Using fMRI: Task-Based vs. Resting-State Patterns and Lifespan Trajectories /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring Human Brain Functional Networks Using fMRI: Task-Based vs. Resting-State Patterns and Lifespan Trajectories // Shuoran Li.
Author:	Li, Shuoran,
Description:	1 electronic resource (177 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31931247
ISBN:	9798314864739

Exploring Human Brain Functional Networks Using fMRI: Task-Based vs. Resting-State Patterns and Lifespan Trajectories /
Li, Shuoran,

Exploring Human Brain Functional Networks Using fMRI: Task-Based vs. Resting-State Patterns and Lifespan Trajectories /Shuoran Li. - 1 electronic resource (177 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

As human life expectancy increases, health issues among the elderly have become an urgent concern. Among these, brain diseases pose a significant challenge due to the difficulty in detecting pathological brain changes at an early stage, often resulting in delayed diagnosis and intervention. To address this challenge, it is essential to understand functional brain networks in healthy individuals, including their patterns under resting and various task-based states and their lifespan trajectories. Such insights are crucial for identifying biomarkers that differentiate between pathological and healthy brain networks. However, due to the complexity and high dimensionality of the brain network, most existing studies examine only a portion of the brain, and their findings are often inconsistent. These issues are primarily attributed to the lack of appropriate statistical approaches and an insufficient sample size to produce reliable results. In this dissertation, we use large-scale functional magnetic resonance imaging (fMRI) data from the Human Connectome Project and propose two novel statistical models to study human brain functional networks in healthy individuals. First, we investigate human brain functional network patterns under resting and various task-based states. By introducing a novel Bayesian framework, we reveal both population-mean and subject-specific whole-brain functional networks for a cohort of one thousand healthy young adults across various states (e.g., resting or task-based). We identify consistent modular structures specialized for different functions while uncovering substantial variations in brain connections across states, individuals, and brain parcellation methods. Second, we investigate the trajectories of functional connectivity (FC) changes across the human lifespan. By developing a clustering-enabled regression approach, we cluster brain regions that undergo identical age-related changes in FC and reveal diverse patterns of these changes for different region clusters. FC between region clusters from the same functional network tends to decrease over time, whereas FC between region clusters from different networks exhibits diverse patterns of age-related changes. Furthermore, we identify sex-specific trends in FC changes. Females show much higher FC within the default mode network, whereas males display higher FC across several more brain networks.

English

ISBN: 9798314864739Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Bayesian modeling

Exploring Human Brain Functional Networks Using fMRI: Task-Based vs. Resting-State Patterns and Lifespan Trajectories /
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520 $a As human life expectancy increases, health issues among the elderly have become an urgent concern. Among these, brain diseases pose a significant challenge due to the difficulty in detecting pathological brain changes at an early stage, often resulting in delayed diagnosis and intervention. To address this challenge, it is essential to understand functional brain networks in healthy individuals, including their patterns under resting and various task-based states and their lifespan trajectories. Such insights are crucial for identifying biomarkers that differentiate between pathological and healthy brain networks. However, due to the complexity and high dimensionality of the brain network, most existing studies examine only a portion of the brain, and their findings are often inconsistent. These issues are primarily attributed to the lack of appropriate statistical approaches and an insufficient sample size to produce reliable results. In this dissertation, we use large-scale functional magnetic resonance imaging (fMRI) data from the Human Connectome Project and propose two novel statistical models to study human brain functional networks in healthy individuals. First, we investigate human brain functional network patterns under resting and various task-based states. By introducing a novel Bayesian framework, we reveal both population-mean and subject-specific whole-brain functional networks for a cohort of one thousand healthy young adults across various states (e.g., resting or task-based). We identify consistent modular structures specialized for different functions while uncovering substantial variations in brain connections across states, individuals, and brain parcellation methods. Second, we investigate the trajectories of functional connectivity (FC) changes across the human lifespan. By developing a clustering-enabled regression approach, we cluster brain regions that undergo identical age-related changes in FC and reveal diverse patterns of these changes for different region clusters. FC between region clusters from the same functional network tends to decrease over time, whereas FC between region clusters from different networks exhibits diverse patterns of age-related changes. Furthermore, we identify sex-specific trends in FC changes. Females show much higher FC within the default mode network, whereas males display higher FC across several more brain networks.
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